ABSTRACT
This chapter provides a tutorial on how to analyze phonetic data with Generalized Additive Mixed Models (GAMs). It presents analyses for three example datasets, all of which address aspects of the phonetics of Taiwan Mandarin. The first dataset contains response time data from an auditory priming experiment. For the second dataset, the chapter focuses on the realizations of Mandarin high-level tones by different speaker groups in Taiwan. The third dataset addresses the ongoing merging of two sets of sibilants in Taiwan Mandarin. GAMs can model time-varying response variables. The linear mixed model can allow the intercept and slope of regression lines for individual subjects to vary, but it cannot handle the case in which functional relations are not linear but instead wiggly. In phonetics, a lot of features that speech clinician measure change over time. These include pitch, intensity and formants. Time is a numeric variable ranging from one to ten in normalized time.
